Macroscale Gradient Research Articles

Designing Electronic Structures of Multiscale Helical Converters for Tailored Ultrabroad Electromagnetic Absorption

HighlightsThe energy conversion mechanism is thoroughly analyzed, with a detailed quantitative characterization of the dissipation capacities of polarization, conduction, and magnetic loss.Inspired by DNA transcription, atom and geometry configurations co-modulating multi-scale helical converters achieve the RLmin of −63.13 dB at 1.29 mm, and the maximum RCS reduction value reach 36.4 dB m2.Orbital coupling, spin and cross polarization synergize to realize a 6.08 GHz EAB, further expanding to ultrabroad electromagnetic wave absorption of 12.16 GHz through metamaterial design.

Mapping the structure-function relationship along macroscale gradients in the human brain

Functional coactivation between human brain regions is partly explained by white matter connections; however, how the structure-function relationship varies by function remains unclear. Here, we reference large data repositories to compute maps of structure-function correspondence across hundreds of specific functions and brain regions. We use natural language processing to accurately predict structure-function correspondence for specific functions and to identify macroscale gradients across the brain that correlate with structure-function correspondence as well as cortical thickness. Our findings suggest structure-function correspondence unfolds along a sensory-fugal organizational axis, with higher correspondence in primary sensory and motor cortex for perceptual and motor functions, and lower correspondence in association cortex for cognitive functions. Our study bridges neuroscience and natural language to describe how structure-function coupling varies by region and function in the brain, offering insight into the diversity and evolution of neural network properties.

Methods for decoding cortical gradients of functional connectivity

Abstract Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

Macroscale intrinsic dynamics are associated with microcircuit function in focal and generalized epilepsies

Epilepsies are a group of neurological disorders characterized by abnormal spontaneous brain activity, involving multiscale changes in brain functional organizations. However, it is not clear to what extent the epilepsy-related perturbations of spontaneous brain activity affect macroscale intrinsic dynamics and microcircuit organizations, that supports their pathological relevance. We collect a sample of patients with temporal lobe epilepsy (TLE) and genetic generalized epilepsy with tonic-clonic seizure (GTCS), as well as healthy controls. We extract massive temporal features of fMRI BOLD time-series to characterize macroscale intrinsic dynamics, and simulate microcircuit neuronal dynamics used a large-scale biological model. Here we show whether macroscale intrinsic dynamics and microcircuit dysfunction are differed in epilepsies, and how these changes are linked. Differences in macroscale gradient of time-series features are prominent in the primary network and default mode network in TLE and GTCS. Biophysical simulations indicate reduced recurrent connection within somatomotor microcircuits in both subtypes, and even more reduced in GTCS. We further demonstrate strong spatial correlations between differences in the gradient of macroscale intrinsic dynamics and microcircuit dysfunction in epilepsies. These results emphasize the impact of abnormal neuronal activity on primary network and high-order networks, suggesting a systematic abnormality of brain hierarchical organization.

Plant size, latitude, and phylogeny explain within-population variability in herbivory.

Interactions between plants and herbivores are central in most ecosystems, but their strength is highly variable. The amount of variability within a system is thought to influence most aspects of plant-herbivore biology, from ecological stability to plant defense evolution. Our understanding of what influences variability, however, is limited by sparse data. We collected standardized surveys of herbivory for 503 plant species at 790 sites across 116° of latitude. With these data, we show that within-population variability in herbivory increases with latitude, decreases with plant size, and is phylogenetically structured. Differences in the magnitude of variability are thus central to how plant-herbivore biology varies across macroscale gradients. We argue that increased focus on interaction variability will advance understanding of patterns of life on Earth.

Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness

Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.

Computationally efficient higher-order three-scale method for nonlocal gradient elasticity problems of heterogeneous structures with multiple spatial scales

In this work, an innovative higher-order three-scale (HOTS) computational approach is developed, which can handle and simulate the nonlocal strain-stress gradient elasticity model of heterogeneous structures with multiple spatial scales. The significant characteristics of this study are: (i) the raw fourth-order nonlocal gradient elasticity equations with three-scale structural characteristics are decoupled as the new multi-scale second-order equations for reducing the continuity requirements for multi-scale computational method. (ii) a new micro-meso-macro coupled HOTS computational model for accurately analyzing the nonlocal gradient elasticity behaviors of heterogeneous structures with three-scale structural configurations is rigorously devised by virtue of multi-scale asymptotic analysis. (iii) the small-scale approximate performance of HOTS solutions is derived in the pointwise sense, that illustrates the crucial indispensability of establishing the HOTS solutions for capturing the micro-scale, meso-scale and macro-scale gradient elasticity behaviors of the heterogeneous structures accurately. (iv) the efficient three-scale numerical algorithm on the basis of finite element method (FEM) is presented at length. Finally, several numerical experiments are carried out to validate the applicability of the established HOTS computational methodology, presenting not only the excellent numerical accuracy, but also the advanced computational efficiency. This work provides a unified three-scale computational framework which enables the accurate analysis and efficient simulation of nonlocal strain-stress gradient elasticity problems of heterogeneous structures with multiple spatial scales, and provides a potential application for practical engineering computation.

Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients.

Functions in higher-order brain regions are the source of extensive debate. Past trends have been to describe the brain in terms of a set of functional modules, especially posterior cortical areas, but a new emerging paradigm focuses on interactions between neighboring representations. In this review, we synthesize emerging evidence that a variety of novel functions in the higher-order brain regions are due to convergence. Convergence of macroscale gradients brings feature-rich representations into close proximity, presenting an opportunity for novel functions to arise. Using the TPJ as an example, we demonstrate that convergent areas have three properties, they: (1) are at the peak of the processing hierarchy, (2) combine the most abstracted representations, and (3) are equidistant from other convergent areas. As information moves from primary sensory cortices to higher-order brain regions, it becomes abstracted and hierarchical. Eventually, these processing gradients converge at a point equally and maximally distant from their sensory origins. This convergence, which produces multifaceted cognitive functions, such as mentalizing another person's thoughts or projecting into a future space, parallels evolutionary and developmental characteristics of such regions, resulting in new cognitive and affective faculties.

Multiscale modeling of cortical gradients: The role of mesoscale circuits for linking macro- and microscale gradients of cortical organization and hierarchical information processing

The gradient concept in neuroscience describes systematic and continuous progressions of features of cortical organization across the entire cortex. Recent multimodal studies revealed a macroscale gradient from primary sensory to transmodal association areas which is linked to increasing representational abstraction along the cortical hierarchy, and which is paralleled by microscale gradients of cytoarchitecture and gene expression profiles. Convergent or divergent evidence from these multimodal studies is then used to support inferences about the existence of one common or multiple scale-specific gradients of hierarchical information processing. This paper evaluates the validity of such inferences within the framework of multiscale modeling. In branches of physics and biology where multiscale modeling techniques are used, the simple averaging of microscale details can introduce errors in macroscale modeling if it ignores structures at the intermediate mesoscales of organization which affect system behavior. Conversely, information about mesoscale structures can be used to determine which microscale details are actually relevant to macroscale behavior. In this paper, I similarly argue that multiscale modeling of cortical gradients needs to take organization of mesoscale circuits into account if it affects the structure-function relation that the models describe. Information about these circuits provides crucial evidence for evaluating inferences from micro- and macroscale data to the role of cortical gradients in hierarchical information processing. My application of the multiscale modeling framework reveals that the gradient concept tracks multiple overlapping progressions of cortical properties, rather than one overall gradient of hierarchical information processing. I support this argument by proposing a mesoscale gradient of connectivity which describes architectural differences between granular and agranular circuits, and which helps us better understand the relation between neural connectivity and hierarchical information processing.

Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as functional networks and gradients. Dynamic analysis techniques have shown that functional connectivity is a mere summary of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain's intrinsic activity that cannot be inferred by functional connectivity or the spatial maps derived from it, such as functional networks and gradients. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale functional gradient, simultaneously across the cerebral cortex and in most other brain regions. In some regions, like the thalamus, the propagation suggests previously-undescribed gradients. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between functional networks is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

Assessing tensile behavior of open-hole variable angle tow composites using a general gradient property simulation methodology

Variable Angle Tow placement is a way to steer individual curvilinear fibers. This work presents the assessment of tensile behavior of open-hole composite laminates with Variable Angle Tow reinforcement. A new multi-scale finite element method, consisting of a microscale unit cell model and a macroscale gradient property model, is developed to simulate Variable Angle Tow structures with various fiber trajectories. The tensile strength and the failure process of open-hole reinforced laminates with Variable Angle Tow reinforcement under tensile loading are predicted and analyzed. Experiments are also conducted to investigate reinforcing efficiency and failure modes of the open-hole laminates. The comparison of predicted and experimental results for the tensile strength and failure modes of T700/Epoxy laminates demonstrates clearly that the mechanical behavior of Variable Angle Tow structure can be simulated very well by the proposed multi-scale model. Moreover, it is found that the tensile strength of Variable Angle Tow laminates is closely related to the eccentricity and it reaches the maximum value only when the trajectories of curvilinear fibers keeps consistent with maximum principal stress trajectories of the open-hole plate.

The thalamic functional gradient and its relationship to structural basis and cognitive relevance

The human thalamus is an integrative hub richly connected with cortical networks, involving diverse cognitive functions. Emerging evidence suggests that multiscale structural and functional gradients integrate various information across modalities into an abstract representation. However, the presence of functional gradients in the thalamus and its relationship to structural properties and cognitive functions remain unknown. We estimated the functional gradients of the thalamus in two independent normal cohorts using a novel diffusion embedding analysis. We identified two main axes of the functional connectivity patterns, and examined associations with thalamic anatomy, morphology, intrinsic geometry, and specific behavioral relevance. We found that the dominant gradient indicated a lateral/medial axis across the thalamus and captured associations with anatomical nuclei and gray matter volume. The second gradient was an anterior/posterior axis and provided a behavioral characterization from lower level perception to higher level cognition. Furthermore, these two gradients strongly correlated with spatial distance, indicating the prominence of intrinsic geometry in functional hierarchies. These findings were replicated in an independent dataset. Overall, our findings suggested that macroscale gradients showed a coordination of structural and functional interactions, with hierarchical organization contributing to behavior characterization.

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

Tracing elevational changes in microbial life and organic carbon sources in soils of the Atacama Desert

Abstract The Atacama Desert frequently serves as model system for tracing life under extremely dry conditions. We hypothesized that traces of life in the Atacama Desert follow distinct micro- and macro-scale gradients such as soil depth and elevation, respectively. Different depth intervals of surface soils (0–1, 1–5, and 5–10 cm) were sampled at five sites along an elevational transect near the Quebrada Aroma, spanning from the hyperarid core of the desert towards the arid Western Andean Precordillera (1300 to 2700 m a.s.l.), and from one additional site in the hyperarid core near Yungay. We determined the contents of major elements, pedogenic minerals and oxides, organic carbon (OC), and its δ13C and δ15N isotopic composition. The presence of living microorganisms was assessed by cultivation, and bacterial community composition was analyzed based on 16S rRNA gene sequencing. Additional information about past and present plant and microbial life was obtained from lipid biomarker analysis. We did not detect consistent micro-scale distributions for most of these proxies within the soils. However, concentrations of OC and of long-chain, plant wax-derived n-alkanes increased in soils along the aridity gradient towards the wetter sites, indicating the presence of past life at places presently not covered by vegetation. Likewise, bacterial abundance and diversity decreased as hyperaridity increased and the microbial community composition changed along the transect, becoming enriched in Actinobacteria. The distributional patterns of phospholipid fatty acids (PLFAs) confirmed the larger bacterial diversity at the higher, more humid sites compared to the drier ones. Archaeal isoprenoid glycerol dialkyl glycerol tetraethers (isoGDGTs) and bacterial branched (br)GDGTs, which can also indicate past life, did not follow a clear elevational trend and were absent at the driest site. Taken together, plant-derived and microbiological markers follow primarily the macro-scaled elevation and aridity gradient. Viable bacteria are present even at the driest sites, while detected biomolecules also indicate past life. The detection of past plant life in nowadays apparently lifeless regions suggests that conditions for life were less hostile in former times.

Decoupling of brain function from structure reveals regional behavioral specialization in humans

The brain is an assembly of neuronal populations interconnected by structural pathways. Brain activity is expressed on and constrained by this substrate. Therefore, statistical dependencies between functional signals in directly connected areas can be expected higher. However, the degree to which brain function is bound by the underlying wiring diagram remains a complex question that has been only partially answered. Here, we introduce the structural-decoupling index to quantify the coupling strength between structure and function, and we reveal a macroscale gradient from brain regions more strongly coupled, to regions more strongly decoupled, than expected by realistic surrogate data. This gradient spans behavioral domains from lower-level sensory function to high-level cognitive ones and shows for the first time that the strength of structure-function coupling is spatially varying in line with evidence derived from other modalities, such as functional connectivity, gene expression, microstructural properties and temporal hierarchy.

Modes of operation: A topographic neural gradient supporting stimulus dependent and independent cognition

Human cognition is flexible - drawing on both sensory input, and representations from memory, to successfully navigate complex environments. Contemporary accounts suggest this flexibility is possible because neural function is organized into a hierarchy. Neural regions are organized along a macroscale gradient, anchored at one end by unimodal systems involved with perception and action, and at the other by transmodal systems, including the default mode network, supporting cognition less directly tied to immediate stimulus input. The current study tested whether this cortical hierarchy captures modes of behaviour that depend on immediate input, as well as those that depend on representations from memory. Participants made decisions regarding the location or identity of shapes using information in the environment (0-back) or from a prior trial (1-back). Using task based imaging we established that, regardless of the nature of the decision, medial and lateral visual cortex were recruited when decisions rely on immediate input, while transmodal regions were recruited when judgments depend on information from the prior trial. Using principal components analysis, we demonstrated that shifting decision-making from perception to memory altered the focus of neural activity from unimodal to transmodal regions (and vice versa). Notably, the more pronounced these shifts in neural activity from unimodal to transmodal regions when decisions relied on memory, the more efficiently individuals performed this task. These data illustrate how the macroscale organization of neural function into a hierarchy allows cognition to rely on input, or information from memory, in a flexible and efficient manner.

Distant from input: Evidence of regions within the default mode network supporting perceptually-decoupled and conceptually-guided cognition

The default mode network supports a variety of mental operations such as semantic processing, episodic memory retrieval, mental time travel and mind-wandering, yet the commonalities between these functions remains unclear. One possibility is that this system supports cognition that is independent of the immediate environment; alternatively or additionally, it might support higher-order conceptual representations that draw together multiple features. We tested these accounts using a novel paradigm that separately manipulated the availability of perceptual information to guide decision-making and the representational complexity of this information. Using task based imaging we established regions that respond when cognition combines both stimulus independence with multi-modal information. These included left and right angular gyri and the left middle temporal gyrus. Although these sites were within the default mode network, they showed a stronger response to demanding memory judgements than to an easier perceptual task, contrary to the view that they support automatic aspects of cognition. In a subsequent analysis, we showed that these regions were located at the extreme end of a macroscale gradient, which describes gradual transitions from sensorimotor to transmodal cortex. This shift in the focus of neural activity towards transmodal, default mode, regions might reflect a process of where the functional distance from specific sensory enables conceptually rich and detailed cognitive states to be generated in the absence of input.

Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity

SummaryCertain organizational features of brain networks present in the individual are lost when central tendencies are examined in the group. Here we investigated the detailed network organization of four individuals each scanned 24 times using MRI. We discovered that the distributed network known as the default network is comprised of two separate networks possessing adjacent regions in eight or more cortical zones. A distinction between the networks is that one is coupled to the hippocampal formation while the other is not. Further exploration revealed that these two networks were juxtaposed with additional networks that themselves fractionate group-defined networks. The collective networks display a repeating spatial progression in multiple cortical zones, suggesting that they are embedded within a broad macroscale gradient. Regions contributing to the newly defined networks are spatially variable across individuals and adjacent to distinct networks, raising issues for network estimation in group-averaged data and applied endeavors, including targeted neuromodulation.

Environmental drivers defining linkages among life-history traits: mechanistic insights from a semiterrestrial amphipod subjected to macroscale gradients.

Determining the existence of interconnected responses among life-history traits and identifying underlying environmental drivers are recognized as key goals for understanding the basis of phenotypic variability. We studied potentially interconnected responses among senescence, fecundity, embryos size, weight of brooding females, size at maturity and sex ratio in a semiterrestrial amphipod affected by macroscale gradients in beach morphodynamics and salinity. To this end, multiple modelling processes based on generalized additive mixed models were used to deal with the spatio-temporal structure of the data obtained at 10 beaches during 22 months. Salinity was the only nexus among life-history traits, suggesting that this physiological stressor influences the energy balance of organisms. Different salinity scenarios determined shifts in the weight of brooding females and size at maturity, having consequences in the number and size of embryos which in turn affected sex determination and sex ratio at the population level. Our work highlights the importance of analysing field data to find the variables and potential mechanisms that define concerted responses among traits, therefore defining life-history strategies.

Numerical experiments on the intermediate asymptotics of shear-free turbulent transport and diffusion

A numerical experiment on the interaction between different decaying homogeneous and isotropic turbulence is described. In the absence of kinetic energy production, the intermediate asymptotics of the turbulent shear-free mixing layer can be observed. The first aim of the experiment is to verify the existence of the intermittency or of the Gaussian asymptotic state in the case of the absence, or weak presence, of a lengthscale gradient. The second aim is to analyse the effects that are due to the difference between the spectral distribution of the interacting turbulence fields, which introduces the presence of the gradient of integral scale into the initial condition. It can be observed that the homogeneity of the integral length across the shearless layer is not a sufficient condition to obtain the Gaussian asymptotic state. In fact, if the macroscale gradient is suppressed by considering turbulence with similar spectra, it is apparent that the intermittency increases with the energy gradient. Furthermore, by independently varying the initial energy level and distribution over the wavenumbers, two turbulence fields can be joined with an initial difference of integral scale either opposite to or concordant with the gradient of the turbulent kinetic energy. It is found that the intermittency and the depth of penetration by the eddies from the high-energy region increase when the energy and lengthscale gradients are concordant and decrease when they are opposite. Therefore, the most efficient process of mixing takes place when the spectra of two mixed fields differ in the lowest wavenumbers.